Machine learning architectures, such as deep neural networks, have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics and drug design. Deep Learning is a class of machine learning algorithms. Maximizing flexibility and cost-efficiency of deep learning algorithms and computations may assist in meeting the needs of deep learning processors, for example, those performing deep learning in a data center.
Matrix multiplication is a key performance/power limiter for many algorithms, including machine learning. Some conventional matrix multiplication approaches are specialized, for example they lack the flexibility to support a variety of data formats (signed and unsigned 8b/16b integer, 16b floating-point) with wide accumulators, and the flexibility to support both dense and sparse matrices.